In situ bio-electrochemical remediation of MTBE-contaminated groundwater at a fuel station in China
Bibliographic record
Abstract
Methyl tert-butyl ether (MTBE) is commonly found in groundwater due to leaking underground storage tanks. This study focuses on MTBE-contaminated groundwater with a long history at a fuel station in Beijing, China, where previous remediation was unsuccessful. This work field-tested a novel bioelectrochemical system (BES) and showcases a “microbial electrochemical snorkel” (MES) mechanism for enhanced in situ biodegradation of MTBE in groundwater. Data from this field study revealed that MTBE, with starting concentrations ranging from 8.9 μg/L to 41,900 μg/L, was effectively biodegraded (up to 97.4 %) in an anaerobic environment within six months. Enhanced biodegradation of petroleum hydrocarbon compounds and the generation of MTBE biodegradation intermediates were also observed. Data of the electrical parameters demonstrated the correlation of BES systems. Redundancy analysis (RDA) showed that BES shifted microbial communities from contaminant-positive correlations to degradation/product-positive correlations. Gene expression analysis indicated that BES enhanced metabolic pathways for biodegradation and upregulated the expression of specific monooxygenases in anaerobic groundwater. These findings demonstrate a robust aerobic-like biodegradation in the anaerobic matrix when the MES mechanism establishes an alternative electron transfer pathway, which facilitates a robust biodegradation of MTBE that would have been otherwise sluggish in an anaerobic matrix like in this study. This work sheds light on a novel passive-active remedy for treating sites with long contamination history and unfavorable geochemical conditions for natural attenuation. • High-performance bioelectrochemical system (BES) for in situ degradation of MTBE. • Pilot demonstration of BES for groundwater remediation at operating fuel stations. • Bioelectrochemical systems induce hydrogeochemical changes of groundwater. • Shifts in metagenomics profiles during BES -enhanced MTBE biodegradation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".